# Using theoretical ROC curves for analysing machine learning binary classifiers

@article{Omar2019UsingTR, title={Using theoretical ROC curves for analysing machine learning binary classifiers}, author={Luma Qassam Abedalqader Omar and Ioannis P. Ivrissimtzis}, journal={Pattern Recognit. Lett.}, year={2019}, volume={128}, pages={447-451} }

Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions $P_s$ and $P_n$ of the response belonging to either class, probability distributions for the cost of each type of misclassification, and compute a performance score from the expected cost.
In machine learning, classifier responses are obtained experimentally and… Expand

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